AirBnB_Zillow_Data_Challenge.utf8

1. Synopsis

Airbnb, Inc. is an online marketplace for arranging or offering lodging, primarily homestays, or tourism experiences. The company does not own any of the real estate listings, nor does it host events; it acts as a broker, receiving commissions from each booking. The company is based in San Francisco, California, United States.

Problem Statement

You are consulting for a Real Estate Company that has a niche in purchasing properties to rent out short-term as part of their business model specifically within New York City. The real estate company has already concluded that two bedroom properties are the most profitable; however, they do not know which zip codes are the best to invest in.
The real estate company has engaged your firm to build out a data product and provide your conclusions to help them understand which Zip Codes would generate the maximum Profit on short term rentals within New York City.

Assumptions

The investor will pay for the property in cash (i.e. no mortgage/interest rate will need to be accounted for).
The time value of money discount rate is 0% (i.e. $1 today is worth the same 100 years from now).
All properties and all square feet within each locale can be assumed to be homogeneous (i.e. a 1000 square foot property in a locale such as Bronx or Manhattan generates twice the revenue and costs twice as much as any other 500 square foot property within that same locale.)
The analysis has been done for a period of 30 years.
The ARIMA Model will be a perfect predictor for forecasting the prices of listings.

2. Packages Required

library(tidyr)
library(DT)
library(ggplot2)
library(dplyr)
library(tidyverse)
library(kableExtra)
library(readxl)
library(scales)
library(RColorBrewer)
library(wesanderson)
library(plotly)
library(data.table)
library(ggpubr)

Package	Description
library(tidyr)	For changing the layout of your data sets, to convert data into the tidy format
library(DT)	For HTML display of data
library(ggplot2)	For customizable graphical representation
library(dplyr)	For data manipulation
library(tidyverse)	Collection of R packages designed for data science that works harmoniously with other packages
library(kableExtra)	To display table in a fancy way
library(readxl)	The readxl package makes it easy to get data out of Excel and into R
library(scales)	The idea of the scales package is to implement scales in a way that is graphics system agnostic
library(RColorBrewer)	RColorBrewer is an R package that allows users to create colourful graphs with pre-made color palettes that visualize data in a clear and distinguishable manner
library(wesanderson)	A Wes Anderson is color palette for R
library(plotly)	Plotly’s R graphing library makes interactive, publication-quality graphs
library(data.table)	Fast aggregation of large data
library(ggpubr)	Grid Based Plots

3. Data Preparation

3.1 Importing the Data

Two datasets have been provided for analysing the problem statement.

Zillow Data: Cost data to determine the average property price for 2 bedrooms
AirBnB Data: Information on the listing including location, number of bedrooms, room types (entire home/private home/shared home)

The above two datasets have been imported for our analysis.

#Reading the Zillow and Listings Data File
home_price <- read.csv("D:/College/Capital One/Data Challenge/Code/Zip_Zhvi_2bedroom.csv") 
listings <- read.csv("D:/College/Capital One/Data Challenge/Code/listings.csv")

3.2 Data Cleaning

3.2.1 Zillow Data File

The following actions have been performed on the Zillow dataset to prepare for analysis:

Our analysis is restricted to the listing in New York City, thus we filter out all the other cities.
Changing the column name of RegionName to zipcode for better understanding.
Apply ARIMA Model to forecast the prices of listings in the year 2020 from the data available.
Creating a new column currentPrice which will hold the currentPrice in the year 2020.

The data which is available to us contains the prices only till the year 2017. Thus, our analysis would be more accurate if we had forecast the price of the listing for the year 2020.

Assuming that there is seasonality in the price and also that values depend not only on previous values (Auto Regressive AR) but also on diferences between previous values (Moving Average MA), we apply ARIMA model to predict the cost of the propeties.

zillow_forecast <- function(tempdf,cityName){ # Zillow data and New york city given as function argument

  # Select zillow cost information from last 5 years and modify zillow data (tempdf) to only hold relevant columns
  n <- 60
  tempdf <- tempdf[,c(2,3,7,(ncol(tempdf) - n):ncol(tempdf))]
 
  tempdf <- filter(tempdf,City == cityName) # Filter for the required city
  
  colnames(tempdf)[colnames(tempdf) == "RegionName"] <- "zipcode" # Set proper column name to be used for merging later
  
  tempdf$currentPrice <- NULL # Create a new column to store the latest price in 2019
  # we define a for loop to iterate over each zipcode to obtain latest cost of property
  for (i in 1:nrow(tempdf)) {

    tmp = ts(as.vector(t(tempdf[,c(4:64)])[,i]),start = c(2012,6),frequency = 12) # Convert the monthly cost data into time series data 
  
    ARIMAfit = arima(tmp, order = c(1,1,1), seasonal = list(order = c(1,0,1),period = NA), 
                     method = "ML")# Define ARIMA model to be used for prediction
    
    pred = predict(ARIMAfit, n.ahead = 20)# use the ARIMA model to predict the price
    
    predval <- pred$pred # Store the predicted values in a variable 
    
    tempdf$currentPrice[i] <- predval[length(predval)] # set the value of current price for the specific zipcode as price in Jan 2019
  }
  return(tempdf[,c(1,2,3,65)]) # return the filtered data containing only relevant columns

}

zillow_final <- zillow_forecast(home_price,"New York")

The data frame zillow_final is ready for our analysis.

3.2.2 Listings Data File

The listings file has 48895 observations and 106 variables. However for our analysis we shall be using columns which are more informative and relevant in analysing the most profitable zipcodes for AirBnb to invest in. Thus, only the following variables are kept for the analysis and the others are dropped.

ID
Zipcode
bedrooms
price
weekly_price
monthly_price
cleaning_fee
neighbourhood_group_cleansed
number_of_reviews
review_scores_rating

relevantcol <- c("id","zipcode","bedrooms","price","weekly_price","monthly_price","cleaning_fee","neighbourhood_group_cleansed","number_of_reviews","review_scores_rating")

listings_fil <- listings[,relevantcol]

Our analysis is restricted only for 2 bedrooms. Thus we filter out listings which has only 2 bedrooms.

listings_fil <- listings_fil %>% filter(bedrooms == 2)

The listings_fil is now ready for our analysis.

3.2.3 Merging and Cleaning

We then merge the two datasets and perform our manipulation and analysis on the merged dataset.

finalData <- merge(listings_fil,zillow_final,by = c("zipcode")) # merge data sets on zipcode
str(finalData)

## 'data.frame':    1565 obs. of  13 variables:
##  $ zipcode                     : Factor w/ 200 levels "","07093","07302",..: 7 7 7 7 7 7 7 7 7 7 ...
##  $ id                          : int  19288937 13866112 31835260 5207973 17843998 4126452 35415489 2150328 31408487 3984168 ...
##  $ bedrooms                    : int  2 2 2 2 2 2 2 2 2 2 ...
##  $ price                       : Factor w/ 674 levels "$0.00","$1,000.00",..: 244 129 208 222 244 189 244 635 640 134 ...
##  $ weekly_price                : Factor w/ 599 levels "","$1,000.00",..: 1 1 1 49 1 1 1 438 1 1 ...
##  $ monthly_price               : Factor w/ 681 levels "","$1,000.00",..: 1 1 1 1 1 1 1 1 1 466 ...
##  $ cleaning_fee                : Factor w/ 207 levels "","$0.00","$1,000.00",..: 13 193 22 163 182 26 26 5 5 44 ...
##  $ neighbourhood_group_cleansed: Factor w/ 5 levels "Bronx","Brooklyn",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ number_of_reviews           : int  99 3 6 1 8 13 4 0 18 11 ...
##  $ review_scores_rating        : int  97 100 93 NA 88 80 100 NA 93 91 ...
##  $ City                        : Factor w/ 4684 levels "Aberdeen","Abilene",..: 2702 2702 2702 2702 2702 2702 2702 2702 2702 2702 ...
##  $ SizeRank                    : int  21 21 21 21 21 21 21 21 21 21 ...
##  $ currentPrice                : num  2171496 2171496 2171496 2171496 2171496 ...

The variable names for the final merged dataset are not self-explanatory. Renaming the column names to make it more self-explanatory.

colnames(finalData) <-  c("zipcode","id","bedrooms","per_night_price","weekly_price","monthly_price","cleaning_fee","neighbourhood","number_of_reviews","review_scores_rating","city","size_rank","current_price")

The factor levels in the City column are incorrect as only New York City is required. Thus we correct the factor level of the City column.

finalData$city <- factor(finalData$city, levels = c("New York"))

All the values in the price columns(per_night_price, weekly_price, monthly_price, cleaning_fee) have a dollar sign stored in them which will hinder in analysis of that column. Thus, we remove the dollar sign from these columns.

Our final merged dataset is now ready for our analysis.

cols <- c("per_night_price", "weekly_price", "monthly_price","cleaning_fee") # selecting columns to be cleaned

# Specify a function to replace characters with whitespace

replace_dollar <- function(x){
  price <- as.numeric(gsub("[$,]","",x)) # this function removes $ from data
  return(price)
}

# Apply function to replace characters with whitespace

finalData[cols] <- lapply(finalData[cols], replace_dollar) # running the above defined function on cols

# Final Check of the structure of the data

str(finalData)

## 'data.frame':    1565 obs. of  13 variables:
##  $ zipcode             : Factor w/ 200 levels "","07093","07302",..: 7 7 7 7 7 7 7 7 7 7 ...
##  $ id                  : int  19288937 13866112 31835260 5207973 17843998 4126452 35415489 2150328 31408487 3984168 ...
##  $ bedrooms            : int  2 2 2 2 2 2 2 2 2 2 ...
##  $ per_night_price     : num  250 165 218 230 250 200 250 850 88 170 ...
##  $ weekly_price        : num  NA NA NA 1200 NA NA NA 6000 NA NA ...
##  $ monthly_price       : num  NA NA NA NA NA NA NA NA NA 4500 ...
##  $ cleaning_fee        : num  110 85 120 60 75 125 125 100 100 150 ...
##  $ neighbourhood       : Factor w/ 5 levels "Bronx","Brooklyn",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ number_of_reviews   : int  99 3 6 1 8 13 4 0 18 11 ...
##  $ review_scores_rating: int  97 100 93 NA 88 80 100 NA 93 91 ...
##  $ city                : Factor w/ 1 level "New York": 1 1 1 1 1 1 1 1 1 1 ...
##  $ size_rank           : int  21 21 21 21 21 21 21 21 21 21 ...
##  $ current_price       : num  2171496 2171496 2171496 2171496 2171496 ...

3.3 Cleaned Dataset

The final cleaned dataset can be found below in an interactive table.

datatable(finalData, filter = 'top')

3.4 Summary of Variables

Variable	Class	Description
zipcode	Factor	Zip code where the property is located.
id	int	Identifier used by AirBnB for the listing
bedrooms	int	Indicates the number of bedrooms within the property.
per_night_price	num	Price the host is charging to stay per night.
weekly_price	num	Weekly price the host is charging; this could be discounted for longer term stays.
monthly_price	num	Monthly price the host is charging; this could be discounted for longer term stays.
cleaning_fee	num	Price the host is charging to clean up after your stay.
neighbourhood	Factor	Neighborhood where the property is located
number_of_reviews	int	Number of reviews received for the property for its entire existence within AirBnB.
review_scores_rating	int	Overall score given based on accuracy, cleanliness, check-in, communication, location, and value.
city	Factor	City where the property is located.
size_rank	int	Population of the area; the lower the number the greater the population.
current_price	num	Current Price of the Listing

4. Exploratory Data Analysis

4.1 Listings Analysis

We first analyze the number of listings available in each of the zipcode. This will give us an idea of the zipcodes which have the maximum number of AirBnb listings.

finalDataWithCount <- finalData %>% 
                      group_by(zipcode) %>% 
                      summarise(count = n())
                      
ggplot(finalDataWithCount, aes(x = reorder(zipcode,-count), y = count)) + 
theme_set(theme_light()) +
geom_bar(stat = "identity", width = .5, fill = "tomato3") +
ggtitle("Number of 2 Bedroom Listings in NYC in each zipcode") + 
xlab("Zipcode") + 
ylab("Number of 2 Bedroom Listings") +
theme(axis.text.x = element_text(angle = 65, vjust = 0.8))

From the above graph, we conclude that the zipcode 11215 has the maximum number of 2 bedroom listings in New York City. Also, the top 10 zipcodes with maximum number of 2 bedroom listings are:

11215
10036
10003
10025
11217
10011
10013
11231
10014
11201

4.2 Neighbourhood Analysis

We now analyze which neighbourhoods in New York City have the maximum number of 2 bedroom listings.

Neighbourhood_Analysis <- finalData %>% 
                          group_by(finalData$neighbourhood) %>% 
                          summarise(count = n())


ggplot(Neighbourhood_Analysis, aes(x = reorder(`finalData$neighbourhood`,count), y = count, fill = `finalData$neighbourhood`)) +
  theme_light() +
  geom_bar(stat = "identity", width = .5, fill = "#1F618D") +
  coord_flip() +
  geom_text(aes(label = count), hjust = 0.0, color = "blue", size = 3.5) +
  ggtitle("Number of 2 Bedroom Listings in each Neighbourhood") + 
  xlab("Neighbourhood") + ylab("Number of 2 Bedroom Listings") + 
  theme(axis.text.x = element_text(angle = 65, vjust = 0.8))

From the graph above, we conlcude the following results:

Manhattan has the maximum number of 2 bedroom listings: 1022, followed by Brooklyn with 501 listings
Staten Island and Queens have a much lesser number of 2 bedroom listing as compared to Manhattan and Brooklyn

4.3 Review Analysis

The number of reviews variable can be used as an estimate to foresee the most popular zipcodes for AirBnb in New York City. The more the number of reviews, more popular are the listings in that zipcode. Thus, we now analyze the zipcodes which have the maximum number of reviews.

finalData %>% group_by(zipcode) %>% summarise(reviews_count = sum(number_of_reviews)) %>% 
ggplot( aes(x = zipcode, y = reviews_count)) +
  geom_segment( aes(x = reorder(zipcode,reviews_count), xend = zipcode, y = 0, yend = reviews_count), color = "blue") +
  geom_point( color = "orange", size = 4 ) +
  theme_classic() +
  coord_flip() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank()
  ) +
  xlab(" Zipcode ") +
  ylab("Number of reviews")

From the graph we can conlcude that the zipcodes with most number of reviews are:

11215
10003
11217
10036
11231
10011
10013
10025
11201
10014

We then visualize the number of reviews grouped by Neighbourhood and also compute the average per night price of the AirBnB listings in those neighbourhoods.

reviews_data <- finalData %>% group_by(neighbourhood, zipcode, size_rank) %>% summarise(reviews_count = sum(number_of_reviews), avg_night_price = round(mean(per_night_price), digits = 2), avg_rating = round(mean(review_scores_rating, na.rm = TRUE), digits = 2))
                                                                                        
review_price_plot <- reviews_data %>% arrange(desc(size_rank)) %>%
                     ggplot(aes(x = reviews_count, y = avg_night_price, size = avg_rating, color = neighbourhood)) +
                     geom_point(alpha = 0.5) +
                     labs(y = "Average Price Per Night in that Zipcode", x = "Total Number of Reviews") + 
                     ggtitle("Number of Reviews and Price for Zipcodes in Each Neighbourhood") +  
                     scale_size(range = c(0, 10), name = "Size Rank(M)") 

ggplotly(review_price_plot)

The size of the bubble represents the mean average rating of listings in that zipcode of the given neighbourhood for the listings. Bigger the bubble, higher the mean user ratings given to the AirBnBs listing in the neighbourhood. From the graph, we can easily conclude that:

The number of reviews in Brooklyn and Manhattan are consideraly much higher than that of Staten Island and Queens
The average price per night stay in neighbourhoods Brooklyn and Manhattan are much higher than that of Queens and Staten Island

4.4 Cost Analysis

In order to analyse the most profitable zipcodes for AirBnB to invest in future, we need to first analyse the cost of investment which is needed to establish an AirBnB listing in that particular zipcode of the neighbourhood.

medianCost <-  finalData %>% 
               select(zipcode,neighbourhood, current_price) %>% 
               filter(neighbourhood == c("Manhattan","Brooklyn","Queens","Staten Island"))  %>% 
               group_by(neighbourhood,zipcode) %>% 
               summarise_all(funs(median)) %>% 
               ggplot(aes(x = reorder(zipcode, -current_price), y = current_price, fill = neighbourhood )) + 
               ggtitle("Average Cost of Listings in various Zipcodes") +
               geom_bar(stat = "identity") + scale_y_continuous(labels = scales::comma) + 
               labs(y = "Average Cost", x = "Zipcode") +
               theme_classic2() + 
               theme(plot.background = element_blank(),panel.grid.major = element_blank(),panel.grid.minor =        element_blank(),panel.border = element_blank(),axis.text.x = element_text(angle = 90, hjust = 1)) + guides(fill = guide_legend(title = "Neighbourhood"))

ggplotly(medianCost)

From the above graph, we can conclude the zipcodes which has the maximum and minimum average price for each neighbourhood.

Manhattan - Highest: 10013, Lowest: 10025
Brooklyn - Highest: 11201, Lowest: 11234
Staten Island - Highest: 10305, Lowest: 10303
Queens - Highest: 11434

4.5 Revenue Analysis

The next stage of the analysis involves calculating the revenue earned per year in each of the zipcodes in New York City. As given in the problem, we have asssumed the occupancy rate for the listings to be 75% (or 0.75).

revenue_analysis <- finalData

occupancy_rate <- 0.75

Year_days <- 365 # number of days in a year

# Generate the revenue for a year per property
revenue_analysis$revenue_per_year <- occupancy_rate*Year_days*revenue_analysis$per_night_price 
                                                          
# Obtain the Revenue/Cost ratio
revenue_analysis$revenue_cost_ratio <- ((revenue_analysis$revenue_per_year)/(revenue_analysis$current_price))

# Breakeven period
revenue_analysis$breakeven_point <- (revenue_analysis$current_price/(revenue_analysis$revenue_per_year - (occupancy_rate*52*revenue_analysis$cleaning_fee)))

# Revenue earned over 5 years
revenue_analysis$year_5 <- ((revenue_analysis$revenue_per_year*5) - (5*occupancy_rate*52*revenue_analysis$cleaning_fee)) - revenue_analysis$current_price

# Revenue earned over 10 years
revenue_analysis$year_10 <- ((revenue_analysis$revenue_per_year*10) - (10*occupancy_rate*52*revenue_analysis$cleaning_fee)) - revenue_analysis$current_price

# Revenue earned over 15 years
revenue_analysis$year_15 <- ((revenue_analysis$revenue_per_year*15) - (5*occupancy_rate*52*revenue_analysis$cleaning_fee)) - revenue_analysis$current_price

# Revenue earned over 20 years
revenue_analysis$year_20 <- ((revenue_analysis$revenue_per_year*20) - (20*occupancy_rate*52*revenue_analysis$cleaning_fee)) - revenue_analysis$current_price

# Revenue earned over 25 years
revenue_analysis$year_25 <- ((revenue_analysis$revenue_per_year*25) - (25*occupancy_rate*52*revenue_analysis$cleaning_fee)) - revenue_analysis$current_price

# Revenue earned over 30 years
revenue_analysis$year_30 <- ((revenue_analysis$revenue_per_year*30) - (30*occupancy_rate*52*revenue_analysis$cleaning_fee)) - revenue_analysis$current_price

revenue_plot <- revenue_analysis %>% 
                select(zipcode,neighbourhood, revenue_per_year) %>% 
                filter(neighbourhood == c("Manhattan","Brooklyn","Queens","Staten Island"))  %>% 
                group_by(neighbourhood,zipcode) %>% 
                summarise_all(funs(median)) %>% 
                ggplot(aes(x = reorder(zipcode, -revenue_per_year), y = revenue_per_year, fill = neighbourhood )) + 
                ggtitle("Average Revenue from Listings in various Zipcodes") +
                geom_bar(stat = "identity") + scale_y_continuous(labels = scales::comma) + 
                labs(y = "Average Revenue", x = "Zipcode") +
                theme_classic() + 
                theme(plot.background = element_blank(),panel.grid.major = element_blank(),panel.grid.minor =          element_blank(),panel.border = element_blank(),axis.text.x = element_text(angle = 90, hjust = 1)) + guides(fill = guide_legend(title = "Neighbourhood"))

ggplotly(revenue_plot)

From the above graph, we can conclude that the ten zipcodes which have the maximum revenue earned per year are:

10011
10014
10013
10022
10036
10023
10003
10028
10025

4.6 Breakeven/Profit Analysis

We now need to analyse the profit earned in the zipcodes of New York City over a period of 30 years. This would help us in concluding which zipcodes will start earning profit the earliest and which zipcodes will earn the maximum profit during this tenure.

For the analysis, I have divided the time into intervals of 5 years, 10 years, 15 years, 20 years, 25 years and 30 years and analysed the profit earned during that tenure.

revenue_cost_ratio <- revenue_analysis %>% 
                      group_by(zipcode, current_price) %>% 
                      summarise( mean_revenue_per_year = round(mean(revenue_per_year, na.rm = TRUE), digits = 2),     mean_revenue_5_year = round(mean(year_5, na.rm = TRUE), digits = 2), mean_revenue_10_year = round(mean(year_10, na.rm = TRUE), digits = 2), mean_revenue_15_year = round(mean(year_15, na.rm = TRUE), digits = 2), mean_revenue_20_year = round(mean(year_20, na.rm = TRUE), digits = 2), mean_revenue_25_year = round(mean(year_25, na.rm = TRUE), digits = 2), mean_revenue_30_year = round(mean(year_30, na.rm = TRUE), digits = 2))

revenue_5_years  <- ggplot(revenue_cost_ratio, aes(y = mean_revenue_5_year , x = zipcode)) + 
                    coord_flip() + 
                    labs(y = "Profit in 5 years", x = "Zipcode") +
                    geom_bar(aes(fill = mean_revenue_5_year < 0), stat = "identity") + scale_fill_manual(guide = FALSE, breaks =                     c(TRUE, FALSE), values = c("red", "green")) 

revenue_10_years <- ggplot(revenue_cost_ratio, aes(y = mean_revenue_10_year , x = zipcode)) + 
                    coord_flip() + 
                    labs(y = "Profit in 10 years", x = "Zipcode") +
                    geom_bar(aes(fill = mean_revenue_10_year < 0), stat = "identity") + scale_fill_manual(guide = FALSE, breaks =                     c(TRUE, FALSE), values = c("red", "green"))

revenue_15_years <- ggplot(revenue_cost_ratio, aes(y = mean_revenue_15_year , x = zipcode)) + 
                    coord_flip() + 
                    labs(y = "Profit in 15 years", x = "Zipcode") +
                    geom_bar(aes(fill = mean_revenue_15_year < 0), stat = "identity") + scale_fill_manual(guide = FALSE, breaks =                     c(TRUE, FALSE), values = c("green", "red")) 

revenue_20_years <- ggplot(revenue_cost_ratio, aes(y = mean_revenue_20_year , x = zipcode)) + 
                    coord_flip() + 
                    labs(y = "Profit in 20 years", x = "Zipcode") +
                    geom_bar(aes(fill = mean_revenue_20_year < 0), stat = "identity") + scale_fill_manual(guide = FALSE, breaks =                     c(TRUE, FALSE), values = c("green", "red")) 

revenue_25_years <- ggplot(revenue_cost_ratio, aes(y = mean_revenue_25_year , x = zipcode)) + 
                    coord_flip() + 
                    labs(y = "Profit in 25 years", x = "Zipcode") +
                    geom_bar(aes(fill = mean_revenue_25_year < 0), stat = "identity") + scale_fill_manual(guide = FALSE, breaks =                     c(TRUE, FALSE), values = c("green", "red")) 

revenue_30_years <- ggplot(revenue_cost_ratio, aes(y = mean_revenue_30_year , x = zipcode)) + 
                    coord_flip() + 
                    labs(y = "Profit in 30 years", x = "Zipcode") +
                    geom_bar(aes(fill = mean_revenue_30_year < 0), stat = "identity") + scale_fill_manual(guide = FALSE, breaks =                     c(TRUE, FALSE), values = c("green", "red")) 

ggarrange(revenue_5_years, revenue_10_years, revenue_15_years, revenue_20_years,revenue_25_years, revenue_30_years, ncol = 3, nrow = 2)

In the above graph, red indicated loss and green indicates profit.

From the above graph, we can conclude the following results:

The overall profit can earliest be seen in a span of 15 years. No listings in any zipcodes earn a profit in 10 years
From the graph of profit earned in 15 years, the zipcodes which will start earning profit the earliest are:
- 11434
- 11234
- 10306
- 10305
- 10304
- 10303 with 11434 earning the maximum profit in 15 years
The trend in profit earned in 20 years remains the same with the addition of 10308 earning profit as well
For profit earned in 25 years, 11434 still earns the maximum profit while 10036 earns pretty close to 11434
However, for profit earned in 30 years, 10036 earns the maximum profit
The zipcodes which do not earn any profit even in 30 years are 10028, 10021 and 10013

5. Results and Future Scope

5.1 Results

From the graphs and results obtained, the top 5 zipcodes which are the best choice for investment are:

10036 in Manhattan
- The profit earned by listings in 10036 over the period of 30 years is amongst the top 5 zipcodes with maximum profit in that period
- The cost of listings in 10036 is considerably quiet low as compared to the other listings in Manhattan
- The zipcode is also very popular as it has the second largest number of listings amongst all the other zipcodes

11434 in Queens
- The number of listings in 11434 is quiet less as compared to other zipcodes but the listings in this zipcode will start earning profit the earliest as compared to all the other zipcodes
- The profit earned by listings in 11434 is the second highest profit earned over a period of 30 years

10025 in Manhattan
- The listings in this zipcode have the cheapest cost amongnst all the other zipcodes in Manhattan
- The profit earned by listings in this zipcode is amongst the top 5 zipcodes over the period of 30 years
- The zipcode is also very popular as it has really high number of listings in Manhattan

11234 in Brooklyn
- The cost of listings in 11234 is the least in Brooklyn
- The listings in 11234 are amongst the listings which start earning the profit earliest and are among the top 5 listings with maximum profit over 30 years

10305 in Staten Island
- The cost of listings in 11234 is very low
- The profit earned over the period of 30 years is fifth highest among all the zipcodes

Thus these 5 zipcodes are the most profitable to invest in. Choosing these 5 zipcodes also helps the company achieve a good coverage over New York City as it covers all the neighbourhoods in New York City which are available in the data set.

5.2 Future Scope

Use Amenities column for better prediction of missing square foot area. Additionally, use it to see how price is impacted when there are more amenities. That impact should be considered to scale rent for the same number of amenities for all properties.
Analyse the importance / role which hosts play in the popularity amongst listings in the various neighbourhoods.
Use Text Mining to analyse and infer results from Text Columns such as Description, etc to see if we can infer any common characteristics which are there in popular AirBnB listings.

AirBnB Short Term Rental Investment Analysis

Vipul Mayank